tensorblock/gemma-2-27b-GGUF

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unsloth/gemma-2-27b - GGUF

This repo contains GGUF format model files for unsloth/gemma-2-27b.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.

Filename	Quant type	File Size	Description
gemma-2-27b-Q2_K.gguf	Q2_K	10.450 GB	smallest, significant quality loss - not recommended for most purposes
gemma-2-27b-Q3_K_S.gguf	Q3_K_S	12.169 GB	very small, high quality loss
gemma-2-27b-Q3_K_M.gguf	Q3_K_M	13.425 GB	very small, high quality loss
gemma-2-27b-Q3_K_L.gguf	Q3_K_L	14.519 GB	small, substantial quality loss
gemma-2-27b-Q4_0.gguf	Q4_0	15.628 GB	legacy; small, very high quality loss - prefer using Q3_K_M
gemma-2-27b-Q4_K_S.gguf	Q4_K_S	15.739 GB	small, greater quality loss
gemma-2-27b-Q4_K_M.gguf	Q4_K_M	16.645 GB	medium, balanced quality - recommended
gemma-2-27b-Q5_0.gguf	Q5_0	18.884 GB	legacy; medium, balanced quality - prefer using Q4_K_M
gemma-2-27b-Q5_K_S.gguf	Q5_K_S	18.884 GB	large, low quality loss - recommended
gemma-2-27b-Q5_K_M.gguf	Q5_K_M	19.408 GB	large, very low quality loss - recommended
gemma-2-27b-Q6_K.gguf	Q6_K	22.344 GB	very large, extremely low quality loss
gemma-2-27b-Q8_0.gguf	Q8_0	28.937 GB	very large, extremely low quality loss - not recommended

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

huggingface-cli download tensorblock/gemma-2-27b-GGUF --include "gemma-2-27b-Q2_K.gguf" --local-dir MY_LOCAL_DIR

If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:

huggingface-cli download tensorblock/gemma-2-27b-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'